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This paper aims to examine the valuation precision of composite models in each of six key industries in South Africa. The objective is to ascertain whether equity-based composite multiples models produce more accurate equity valuations than optimal equity-based, single-factor multiples models.

This study applied principal component regression and various mathematical optimisation methods to test the valuation precision of equity-based composite multiples models vis-à-vis equity-based, single-factor multiples models.

The findings confirmed that equity-based composite multiples models consistently produced valuations that were substantially more accurate than those of single-factor multiples models for the period between 2001 and 2010. The research results indicated that composite models produced up to 67 per cent more accurate valuations than single-factor multiples models for the period between 2001 and 2010, which represents a substantial gain in valuation precision.

The evidence, therefore, suggests that equity-based composite modelling may offer substantial gains in valuation precision over single-factor multiples modelling.

In light of the fact that analysts’ reports typically contain various different multiples, it seems prudent to consider the inclusion of composite models as a more accurate alternative.

This study adds to the existing body of knowledge on the multiples-based approach to equity valuations by presenting composite modelling as a more accurate alternative to the conventional single-factor, multiples-based modelling approach.

The purpose of this paper is to change the traditional supplier selection methods by shifting the emphasis from using a single model to using multiple models in the unstructured decision‐making context and to provide a tool for decision makers to make informed decisions of supplier selection in the multiple perspectives.

There are various supplier selection models available in the literature. However, using the result of a single model as a basis for making the final decision could lead to a biased decision given the fact that any model has its limitations. The qualities of the decision‐making process and the decision itself increase by applying a multiple perspectives approach rather than a single model. The multiple perspectives decision‐making allows collaboration and knowledge sharing among the participants which leads to a less‐biased decision. This study examines commonly applied supplier selection models, formulates general perspectives of these models, and proposes a framework of multiple perspectives decision making for supplier selection. It further provides a structure of supplier selection system based on the proposed approach. Through a prototype of web portal, the study demonstrates the usefulness of the proposed multiple perspective system approach in the decision context of collaboration and knowledge sharing.

The general finding from this study is that the multiple perspectives approach to supplier selection enables the decision makers to actively participate and fully understand the decision‐making process through knowledge sharing which in turn ensures high quality of the final decisions.

Supplier selection decision makers can make more informed decisions through collaboration among all decision‐making participants in the multiple perspectives. It informs supply chain managers of the potentially positive effect of knowledge sharing on the decision‐making process in supplier selection.

Multiple perspectives decision making provides a novel approach that emphasizes on the knowledge sharing and collaboration between the experts, who are familiar with the supplier relations, and the decision makers who are responsible for the final decisions.

This study presents finite element (FE) and generalized regression neural network (GRNN) approaches for modeling multiple crack growth problems and predicting crack-growth directions under the influence of multiple crack parameters.

To determine the crack-growth direction in aluminum specimens, multiple crack parameters representing some degree of crack propagation complexity, including crack length, inclination angle, offset and distance, were examined. FE method models were developed for multiple crack growth simulations. To capture the complex relationships among multiple crack-growth variables, GRNN models were developed as nonlinear regression models. Six input variables and one output variable comprising 65 training and 20 test datasets were established.

The FE model could conveniently simulate the crack-growth directions. However, several multiple crack parameters could affect the simulation accuracy. The GRNN offers a reliable method for modeling the growth of multiple cracks. Using 76% of the total dataset, the NN model attained an R2 value of 0.985.

The models are presented for static multiple crack growth problems. No material anisotropy is observed.

In practical crack-growth analyses, the NN approach provides significant benefits and savings.

The proposed GRNN model is simple to develop and accurate. Its performance was superior to that of other NN models. This model is also suitable for modeling multiple crack growths with arbitrary geometries. The proposed GRNN model demonstrates its prediction capability with a simpler learning process, thus producing efficient multiple crack growth predictions and assessments.

The purpose of this paper is to study the parameters' properties of GM(n, h) model on the basis of multiple transformation and the relationship of GM(n, h) model and other grey models.

Multiple transformation property of parameters is important to construct a grey model. However, there is no research on the property of GM(n, h) model, therefore it is meaningful to study the relationship between GM(n, h) model and other grey models.

The multiple transformation property of parameters of GM(n, h) model is recognized. The parameters' value is dependent on multiple transformation value. The values of simulative and predicative are only dependent to the multiple transformation of the main variable and independent to other variables.

The properties of other grey models could be obtained by analyzing the property of GM(n, h) model.

It is a very useful result for constructing a grey model.

This paper discusses multiple transformation property of GM(n, h) model and the relationship between the GM(n, h) model and other grey models. These grey models are put into a common model and the affections that parameters' multiple transformation caused to the model are studied.

Many consumer choice situations are characterized by the simultaneous demand for multiple alternatives that are imperfect substitutes for one another. A simple and parsimonious multiple discrete-continuous extreme value (MDCEV) econometric approach to handle such multiple discreteness was formulated by Bhat (2005) within the broader Kuhn–Tucker (KT) multiple discrete-continuous economic consumer demand model of Wales and Woodland (1983). In this chapter, the focus is on presenting the basic MDCEV model structure, discussing its estimation and use in prediction, formulating extensions of the basic MDCEV structure, and presenting applications of the model. The paper examines several issues associated with the MDCEV model and other extant KT multiple discrete-continuous models. Specifically, the paper discusses the utility function form that enables clarity in the role of each parameter in the utility specification, presents identification considerations associated with both the utility functional form as well as the stochastic nature of the utility specification, extends the MDCEV model to the case of price variation across goods and to general error covariance structures, discusses the relationship between earlier KT-based multiple discrete-continuous models, and illustrates the many technical nuances and identification considerations of the multiple discrete-continuous model structure. Finally, we discuss the many applications of MDCEV model and its extensions in various fields.

Sponsorship rarely occurs in a one sponsor-one sponsee dyad (single sponsorship), yet a large portion of sponsorship research takes this perspective. The purpose of this paper is to propose a model that reflects the complexity and rich diversity inherent in the field. The sponsorship network model considers the plurality of stakeholders to a sponsorship and their potential relationships to each other.

This conceptual paper develops a theoretical and conceptual framework to better identify the effect of sponsorship networks on consumer behavior.

Based on a review of the multiple sponsorships literature, the authors propose an innovative theoretical framework and a set of research propositions. The model considers simultaneously the potential relations between sponsors, sponsees and ambushers at the cognitive, affective and conative levels.

This research emphasizes the managerial implications for stakeholders involved in sponsorship and ambush marketing actions in order to maximize their investment. The model provides a comprehensive understanding of the complex nature of sponsorship networks and their ability to influence consumer behaviors. These effects are more complex than is currently recognized.

The paper aims to model multiple-period market risk forecasts under long memory persistence in market volatility.

The paper proposes volatility forecasts based on a combination of the GARCH(1,1)-model with potentially fat-tailed and skewed innovations and a long memory specification of the slowly declining influence of past volatility shocks. As the square-root-of-time rule is known to be mis-specified, the GARCH setting of Drost and Nijman is used as benchmark model. The empirical study of equity market risk is based on daily returns during the period January 1975 to December 2010. The out-of-sample accuracy of VaR predictions is studied for 5, 10, 20 and 60 trading days.

The long memory scaling approach remarkably improves VaR forecasts for the longer horizons. This result is only in part due to higher predicted risk levels. Ex post calibration to equal unconditional VaR levels illustrates that the approach also enhances efficiency in allocating VaR capital through time.

The improved VaR forecasts show that one should account for long memory when calibrating risk models.

The paper models single-period returns rather than choosing the simpler approach of modeling lower-frequency multiple-period returns for long-run volatility forecasting. The approach considers long memory in volatility and has two main advantages: it yields a consistent set of volatility predictions for various horizons and VaR forecasting accuracy is improved.

Artificial neural network (ANN) models, part of the discipline of machine learning and artificial intelligence, are becoming more popular in the marketing literature and in marketing practice. This paper aims to provide a series of tests between ANN models and competing predictive models.

A total of 46 pairs of models were evaluated in an objective model-building environment. Either logistic regression or multiple regression models were developed and then were compared to ANN models using the same set of input variables. Three sets of B2B data were used to test the models. Emphasis also was placed on evaluating small samples.

ANN models tend to generate model predictions that are more accurate or the same as logistic regression models. However, when ANN models are compared to multiple regression models, the results are mixed. For small sample sizes, the modeling results are the same as for larger samples.

Like all marketing research, this application is limited by the methods and the data used to conduct the research. The findings strongly suggest that, because of their predictive accuracy, ANN models will have an important role in the future of B2B marketing research and model-building applications.

ANN models should be carefully considered for potential use in marketing research and model-building applications by B2B academics and practitioners alike.

The research contributes to the B2B marketing literature by providing a more rigorous test on ANN models using B2B data than has been conducted before.

Reviews the three sales forecasting models most commonly applied in retail site evaluation: multiple regression analysis; multiple discriminant analysis; gravity models. Discusses the important issues involved in the development and application of these methods – including their respective strengths and weaknesses. Key points are that there is no “black box” method and that in the real world of retailing the methods reduce, but do not remove, the need for practical, subjective analysis.

The objective of this paper is twofold: first, to model the value of the firm in the presence of contingent capital and multiple growth options over its life cycle in a stochastic universe to ensure financial stability and recover losses in case of default and second, to clarify how contingent convertible (CoCo) bonds as financial instruments impact the leverage-ratio policies, inefficiencies generated by debt overhang and asset substitution for a firm that has multiple growth options. Additionally, what is its impact on investment timing, capital structure and asset volatility?

The current paper elaborates the modeling of a dynamic problem with respect to the interaction between funding and investment policies during multiple sequential investment cycles simultaneously with dynamic funding. The authors model the value of the firm in the presence of contingent capital that provides flexibility in dealing with default risks as well as growth options in a stochastic universe. The authors examine the firm's closed-form solutions at each stage of its decision-making process before and after the exercise of the growth options (with and without conversion of CoCo) through applying the backward indication method and the risk-neutral pricing theory.

The numerical results show that inefficiencies related to debt overhang and asset substitution can go down with a higher conversion ratio and a larger number of growth options. Additionally, the authors’ analysis reveals that the firm systematically opts for conservative leverage to minimize the effect of debt overhang on decisions so as to exercise growth options in the future. However, the capital structure of the firm has a substantial effect on the leverage ratio and the asset substitution. In fact, the effect of the leverage ratio and the risk-shifting incentive will be greater when the capital structure changes during the firm's decision-making process. Contrarily to traditional corporate finance theory, the study displays that the value of the firm before the investment expansion decreases and then increases with asset volatility, instead of decreasing overall with asset volatility.

The study’s findings reveal that funding, default and conversion decisions have crucial implications on growth option exercise decisions and leverage ratio policy. The model also shows that the firm consistently chooses conservative leverage to reduce the effect of debt overhang on decisions to exercise growth options in the future. The risk-shifting incentive and the debt overhang inefficiency basically decrease with a higher conversion ratio and multiple growth options. However, the effect of the leverage ratio and the risk-shifting incentive will be greater when the capital structure changes during the firm's decision-making process.

The firm's composition between assets in place and growth options evolves endogenously with its investment opportunity and growth option financing, as well as its default decision. In contrast to the standard capital structure models of Leland (1994), the model reveals that both exogenous conversion decisions and endogenous default decisions have significant implications for firms' growth option exercise decisions and debt policies. The model induces some predictions about the dynamics of the firm's choice of leverage as well as the link between the dynamics of leverage and the firm's life cycle.